Job Description
Description :
Key Responsibilities :
- Design, build, and maintain scalable data pipelines and enterprise data platforms
- Develop robust ETL/ELT workflows and automate data engineering processes
- Work on distributed data processing using Spark and large-scale data frameworks
- Optimize and manage cloud-based data engineering solutions across AWS, Azure, or GCP
- Implement orchestration workflows using Airflow or similar tools
- Ensure data quality, governance, security, and compliance standards
- Collaborate with analytics, AI/ML, and business teams for data integration and delivery
- Support real-time and batch data processing requirements
- Troubleshoot data pipeline failures and optimize performance
- Contribute to data architecture modernization and engineering best practices
Required Skills & Experience :
- 8+ years of experience in Data Engineering
- Strong proficiency in Python and Advanced SQL
- Hands-on experience with Spark and distributed processing frameworks
- Experience working with cloud platforms such as AWS, Azure, or GCP
- Expertise in Airflow or other orchestration tools
- Strong understanding of data pipeline automation
- Experience with data governance and data quality practices
- Excellent analytical, troubleshooting, and problem-solving skills
Good to Have :
- Experience with Kafka or Kinesis streaming platforms
- Knowledge of Feature Stores and ML data infrastructure
- Exposure to LLM-ready data pipelines
- Understanding of MLOps concepts and workflows
Preferred Candidate Profile :
- Strong understanding of modern data architecture and scalable systems
- Ability to work in fast-paced and collaborative environments
- Experience supporting AI/ML and analytics use cases
- Strong communication and stakeholder management skills
No Referrers Available
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